Augmented Reality (AR) offers powerful visualization capabilities for industrial robot training, yet current interfaces remain predominantly static, failing to account for learners' diverse cognitive profiles. In this paper, we present an AR application for robot training and propose a multi-agent AI framework for future integration that bridges the gap between static visualization and pedagogical intelligence. We report on the evaluation of the baseline AR interface with 36 participants performing a robotic pick-and-place task. While overall usability was high, notable disparities in task duration and learner characteristics highlighted the necessity for dynamic adaptation. To address this, we propose a multi-agent framework that orchestrates multiple components to perform complex preprocessing of multimodal inputs (e.g., voice, physiology, robot data) and adapt the AR application to the learner's needs. By utilizing autonomous Large Language Model (LLM) agents, the proposed system would dynamically adapt the learning environment based on advanced LLM reasoning in real-time.

翻译：增强现实（AR）为工业机器人训练提供了强大的可视化能力，然而当前界面仍主要保持静态，未能充分考虑学习者多样化的认知特征。本文提出了一种用于机器人训练的AR应用，并设计了一个面向未来集成的多智能体AI框架，以弥合静态可视化与教学智能之间的鸿沟。我们通过对36名参与者执行机器人抓放任务的基线AR界面评估发现：虽然整体可用性较高，但任务时长与学习者特征间的显著差异凸显了动态自适应的必要性。为此，我们提出一种多智能体框架，通过协调多个组件对多模态输入（如语音、生理数据、机器人数据）进行复杂预处理，并使AR应用适配学习者的需求。该框架利用自主大型语言模型（LLM）智能体，能够基于实时的高级LLM推理动态调整学习环境。